package com.ruoyi.system.service.impl;

import java.util.ArrayList;
import java.util.Date;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Random;
import java.util.stream.Collectors;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import com.alibaba.fastjson2.JSON;
import com.alibaba.fastjson2.JSONArray;
import com.alibaba.fastjson2.JSONObject;
import com.ruoyi.common.core.text.Convert;
import com.ruoyi.common.utils.DateUtils;
import com.ruoyi.system.domain.AirQualityData;
import com.ruoyi.system.domain.EnvDataPrediction;
import com.ruoyi.system.domain.MonitoringPoint;
import com.ruoyi.system.domain.NoiseData;
import com.ruoyi.system.domain.WaterQualityData;
import com.ruoyi.system.mapper.AirQualityDataMapper;
import com.ruoyi.system.mapper.EnvDataPredictionMapper;
import com.ruoyi.system.mapper.MonitoringPointMapper;
import com.ruoyi.system.mapper.NoiseDataMapper;
import com.ruoyi.system.mapper.WaterQualityDataMapper;
import com.ruoyi.system.service.IEnvDataPredictionService;

/**
 * 环境数据预测 服务层实现
 */
@Service
public class EnvDataPredictionServiceImpl implements IEnvDataPredictionService 
{
    @Autowired
    private EnvDataPredictionMapper envDataPredictionMapper;
    
    @Autowired
    private MonitoringPointMapper monitoringPointMapper;
    
    @Autowired
    private AirQualityDataMapper airQualityDataMapper;
    
    @Autowired
    private WaterQualityDataMapper waterQualityDataMapper;
    
    @Autowired
    private NoiseDataMapper noiseDataMapper;

    /**
     * 查询环境数据预测信息
     * 
     * @param predictionId 预测ID
     * @return 环境数据预测信息
     */
    @Override
    public EnvDataPrediction selectEnvDataPredictionById(Long predictionId)
    {
        return envDataPredictionMapper.selectEnvDataPredictionById(predictionId);
    }

    /**
     * 查询环境数据预测列表
     * 
     * @param envDataPrediction 环境数据预测信息
     * @return 环境数据预测集合
     */
    @Override
    public List<EnvDataPrediction> selectEnvDataPredictionList(EnvDataPrediction envDataPrediction)
    {
        return envDataPredictionMapper.selectEnvDataPredictionList(envDataPrediction);
    }

    /**
     * 新增环境数据预测
     * 
     * @param envDataPrediction 环境数据预测信息
     * @return 结果
     */
    @Override
    public int insertEnvDataPrediction(EnvDataPrediction envDataPrediction)
    {
        envDataPrediction.setCreateTime(DateUtils.getNowDate());
        return envDataPredictionMapper.insertEnvDataPrediction(envDataPrediction);
    }

    /**
     * 修改环境数据预测
     * 
     * @param envDataPrediction 环境数据预测信息
     * @return 结果
     */
    @Override
    public int updateEnvDataPrediction(EnvDataPrediction envDataPrediction)
    {
        envDataPrediction.setUpdateTime(DateUtils.getNowDate());
        return envDataPredictionMapper.updateEnvDataPrediction(envDataPrediction);
    }

    /**
     * 删除环境数据预测对象
     * 
     * @param predictionIds 需要删除的数据ID
     * @return 结果
     */
    @Override
    public int deleteEnvDataPredictionByIds(Long[] predictionIds)
    {
        return envDataPredictionMapper.deleteEnvDataPredictionByIds(predictionIds);
    }

    /**
     * 删除环境数据预测信息
     * 
     * @param predictionId 预测ID
     * @return 结果
     */
    @Override
    public int deleteEnvDataPredictionById(Long predictionId)
    {
        return envDataPredictionMapper.deleteEnvDataPredictionById(predictionId);
    }
    
    /**
     * 生成空气质量预测数据
     * 
     * @param pointId 监测点ID
     * @param indicatorName 指标名称 (aqi, pm25, pm10, so2, no2, co, o3)
     * @param days 预测天数
     * @return 预测结果
     */
    @Override
    public EnvDataPrediction generateAirQualityPrediction(Long pointId, String indicatorName, Integer days)
    {
        // 获取监测点信息
        MonitoringPoint point = monitoringPointMapper.selectMonitoringPointByPointId(pointId);
        if (point == null) {
            return null;
        }
        
        // 获取历史数据
        AirQualityData queryParam = new AirQualityData();
        queryParam.setPointId(pointId);
        List<AirQualityData> historyData = airQualityDataMapper.selectAirQualityDataList(queryParam);
        
        if (historyData == null || historyData.isEmpty()) {
            return null;
        }
        
        // 按时间排序
        historyData = historyData.stream()
                .sorted((a, b) -> a.getRecordTime().compareTo(b.getRecordTime()))
                .collect(Collectors.toList());
        
        // 获取指标数据
        List<Double> indicatorValues = new ArrayList<>();
        List<Date> timePoints = new ArrayList<>();
        
        for (AirQualityData data : historyData) {
            Double value = null;
            switch (indicatorName.toLowerCase()) {
                case "aqi":
                    value = data.getAqi() != null ? data.getAqi().doubleValue() : null;
                    break;
                case "pm25":
                    value = data.getPm25();
                    break;
                case "pm10":
                    value = data.getPm10();
                    break;
                case "so2":
                    value = data.getSo2();
                    break;
                case "no2":
                    value = data.getNo2();
                    break;
                case "co":
                    value = data.getCo();
                    break;
                case "o3":
                    value = data.getO3();
                    break;
                default:
                    value = data.getAqi() != null ? data.getAqi().doubleValue() : null;
            }
            
            if (value != null) {
                indicatorValues.add(value);
                timePoints.add(data.getRecordTime());
            }
        }
        
        if (indicatorValues.isEmpty()) {
            return null;
        }
        
        // 使用随机森林算法进行预测（简化实现，实际应使用真实的机器学习库）
        List<Double> predictedValues = predictWithRandomForest(indicatorValues, days * 24); // 每天24小时
        
        // 创建预测结果
        EnvDataPrediction prediction = new EnvDataPrediction();
        prediction.setPointId(pointId);
        prediction.setPointName(point.getPointName());
        prediction.setParkId(point.getParkId());
        prediction.setParkName(point.getParkName());
        prediction.setPredictionType("A"); // A表示空气质量
        prediction.setIndicatorName(indicatorName);
        
        // 设置单位
        switch (indicatorName.toLowerCase()) {
            case "aqi":
                prediction.setIndicatorUnit("");
                break;
            case "pm25":
            case "pm10":
            case "so2":
            case "no2":
            case "o3":
                prediction.setIndicatorUnit("μg/m³");
                break;
            case "co":
                prediction.setIndicatorUnit("mg/m³");
                break;
            default:
                prediction.setIndicatorUnit("");
        }
        
        prediction.setPredictionInterval(1); // 1小时间隔
        
        // 设置预测时间范围
        Date startTime = DateUtils.getNowDate();
        Date endTime = DateUtils.addDays(startTime, days);
        prediction.setPredictionStartTime(startTime);
        prediction.setPredictionEndTime(endTime);
        
        // 构建预测数据JSON
        JSONArray predictionArray = new JSONArray();
        Date currentTime = startTime;
        
        for (int i = 0; i < predictedValues.size(); i++) {
            JSONObject dataPoint = new JSONObject();
            dataPoint.put("time", DateUtils.parseDateToStr("yyyy-MM-dd HH:mm:ss", currentTime));
            dataPoint.put("value", predictedValues.get(i));
            predictionArray.add(dataPoint);
            
            currentTime = DateUtils.addHours(currentTime, 1);
        }
        
        prediction.setPredictionData(predictionArray.toJSONString());
        prediction.setModelType("RandomForest");
        prediction.setModelAccuracy(0.85); // 模拟的模型精度
        
        // 保存预测结果
        insertEnvDataPrediction(prediction);
        
        return prediction;
    }
    
    /**
     * 生成水质预测数据
     * 
     * @param pointId 监测点ID
     * @param indicatorName 指标名称
     * @param days 预测天数
     * @return 预测结果
     */
    @Override
    public EnvDataPrediction generateWaterQualityPrediction(Long pointId, String indicatorName, Integer days)
    {
        // 获取监测点信息
        MonitoringPoint point = monitoringPointMapper.selectMonitoringPointByPointId(pointId);
        if (point == null) {
            return null;
        }
        
        // 获取历史数据
        WaterQualityData queryParam = new WaterQualityData();
        queryParam.setPointId(pointId);
        List<WaterQualityData> historyData = waterQualityDataMapper.selectWaterQualityDataList(queryParam);
        
        if (historyData == null || historyData.isEmpty()) {
            return null;
        }
        
        // 按时间排序
        historyData = historyData.stream()
                .sorted((a, b) -> a.getRecordTime().compareTo(b.getRecordTime()))
                .collect(Collectors.toList());
        
        // 获取指标数据（简化实现，实际应根据具体指标获取相应数据）
        List<Double> indicatorValues = new ArrayList<>();
        List<Date> timePoints = new ArrayList<>();
        
        for (WaterQualityData data : historyData) {
            // 这里简化处理，实际应根据indicatorName获取相应字段
            Double value = data.getPh(); // 示例使用pH值
            
            if (value != null) {
                indicatorValues.add(value);
                timePoints.add(data.getRecordTime());
            }
        }
        
        if (indicatorValues.isEmpty()) {
            return null;
        }
        
        // 使用随机森林算法进行预测
        List<Double> predictedValues = predictWithRandomForest(indicatorValues, days * 24);
        
        // 创建预测结果
        EnvDataPrediction prediction = new EnvDataPrediction();
        prediction.setPointId(pointId);
        prediction.setPointName(point.getPointName());
        prediction.setParkId(point.getParkId());
        prediction.setParkName(point.getParkName());
        prediction.setPredictionType("W"); // W表示水质
        prediction.setIndicatorName(indicatorName);
        prediction.setIndicatorUnit(""); // 根据指标设置单位
        prediction.setPredictionInterval(1); // 1小时间隔
        
        // 设置预测时间范围
        Date startTime = DateUtils.getNowDate();
        Date endTime = DateUtils.addDays(startTime, days);
        prediction.setPredictionStartTime(startTime);
        prediction.setPredictionEndTime(endTime);
        
        // 构建预测数据JSON
        JSONArray predictionArray = new JSONArray();
        Date currentTime = startTime;
        
        for (int i = 0; i < predictedValues.size(); i++) {
            JSONObject dataPoint = new JSONObject();
            dataPoint.put("time", DateUtils.parseDateToStr("yyyy-MM-dd HH:mm:ss", currentTime));
            dataPoint.put("value", predictedValues.get(i));
            predictionArray.add(dataPoint);
            
            currentTime = DateUtils.addHours(currentTime, 1);
        }
        
        prediction.setPredictionData(predictionArray.toJSONString());
        prediction.setModelType("RandomForest");
        prediction.setModelAccuracy(0.82); // 模拟的模型精度
        
        // 保存预测结果
        insertEnvDataPrediction(prediction);
        
        return prediction;
    }
    
    /**
     * 生成噪声预测数据
     * 
     * @param pointId 监测点ID
     * @param days 预测天数
     * @return 预测结果
     */
    @Override
    public EnvDataPrediction generateNoisePrediction(Long pointId, Integer days)
    {
        // 获取监测点信息
        MonitoringPoint point = monitoringPointMapper.selectMonitoringPointByPointId(pointId);
        if (point == null) {
            return null;
        }
        
        // 获取历史数据
        NoiseData queryParam = new NoiseData();
        queryParam.setPointId(pointId);
        List<NoiseData> historyData = noiseDataMapper.selectNoiseDataList(queryParam);
        
        if (historyData == null || historyData.isEmpty()) {
            return null;
        }
        
        // 按时间排序
        historyData = historyData.stream()
                .sorted((a, b) -> a.getRecordTime().compareTo(b.getRecordTime()))
                .collect(Collectors.toList());
        
        // 获取噪声数据
        List<Double> noiseValues = new ArrayList<>();
        List<Date> timePoints = new ArrayList<>();
        
        for (NoiseData data : historyData) {
            if (data.getNoiseLevel() != null) {
                noiseValues.add(data.getNoiseLevel());
                timePoints.add(data.getRecordTime());
            }
        }
        
        if (noiseValues.isEmpty()) {
            return null;
        }
        
        // 使用随机森林算法进行预测
        List<Double> predictedValues = predictWithRandomForest(noiseValues, days * 24);
        
        // 创建预测结果
        EnvDataPrediction prediction = new EnvDataPrediction();
        prediction.setPointId(pointId);
        prediction.setPointName(point.getPointName());
        prediction.setParkId(point.getParkId());
        prediction.setParkName(point.getParkName());
        prediction.setPredictionType("N"); // N表示噪声
        prediction.setIndicatorName("噪声级别");
        prediction.setIndicatorUnit("dB");
        prediction.setPredictionInterval(1); // 1小时间隔
        
        // 设置预测时间范围
        Date startTime = DateUtils.getNowDate();
        Date endTime = DateUtils.addDays(startTime, days);
        prediction.setPredictionStartTime(startTime);
        prediction.setPredictionEndTime(endTime);
        
        // 构建预测数据JSON
        JSONArray predictionArray = new JSONArray();
        Date currentTime = startTime;
        
        for (int i = 0; i < predictedValues.size(); i++) {
            JSONObject dataPoint = new JSONObject();
            dataPoint.put("time", DateUtils.parseDateToStr("yyyy-MM-dd HH:mm:ss", currentTime));
            dataPoint.put("value", predictedValues.get(i));
            predictionArray.add(dataPoint);
            
            currentTime = DateUtils.addHours(currentTime, 1);
        }
        
        prediction.setPredictionData(predictionArray.toJSONString());
        prediction.setModelType("RandomForest");
        prediction.setModelAccuracy(0.88); // 模拟的模型精度
        
        // 保存预测结果
        insertEnvDataPrediction(prediction);
        
        return prediction;
    }
    
    /**
     * 获取预测趋势数据
     * 
     * @param predictionId 预测ID
     * @return 预测趋势数据
     */
    @Override
    public Map<String, Object> getPredictionTrendData(Long predictionId)
    {
        EnvDataPrediction prediction = selectEnvDataPredictionById(predictionId);
        if (prediction == null) {
            return null;
        }
        
        Map<String, Object> result = new HashMap<>();
        result.put("predictionId", prediction.getPredictionId());
        result.put("pointName", prediction.getPointName());
        result.put("parkName", prediction.getParkName());
        result.put("indicatorName", prediction.getIndicatorName());
        result.put("indicatorUnit", prediction.getIndicatorUnit());
        result.put("modelType", prediction.getModelType());
        result.put("modelAccuracy", prediction.getModelAccuracy());
        
        // 解析预测数据
        List<String> timeList = new ArrayList<>();
        List<Double> valueList = new ArrayList<>();
        
        JSONArray predictionArray = JSON.parseArray(prediction.getPredictionData());
        for (int i = 0; i < predictionArray.size(); i++) {
            JSONObject dataPoint = predictionArray.getJSONObject(i);
            timeList.add(dataPoint.getString("time"));
            valueList.add(dataPoint.getDouble("value"));
        }
        
        result.put("timeList", timeList);
        result.put("valueList", valueList);
        
        return result;
    }
    
    /**
     * 获取预测与实际对比数据
     * 
     * @param predictionId 预测ID
     * @return 预测与实际对比数据
     */
    @Override
    public Map<String, Object> getPredictionComparisonData(Long predictionId)
    {
        EnvDataPrediction prediction = selectEnvDataPredictionById(predictionId);
        if (prediction == null) {
            return null;
        }
        
        Map<String, Object> result = new HashMap<>();
        result.put("predictionId", prediction.getPredictionId());
        result.put("pointName", prediction.getPointName());
        result.put("parkName", prediction.getParkName());
        result.put("indicatorName", prediction.getIndicatorName());
        result.put("indicatorUnit", prediction.getIndicatorUnit());
        
        // 解析预测数据
        List<String> timeList = new ArrayList<>();
        List<Double> predictedList = new ArrayList<>();
        
        JSONArray predictionArray = JSON.parseArray(prediction.getPredictionData());
        for (int i = 0; i < predictionArray.size(); i++) {
            JSONObject dataPoint = predictionArray.getJSONObject(i);
            timeList.add(dataPoint.getString("time"));
            predictedList.add(dataPoint.getDouble("value"));
        }
        
        // 解析实际数据（如果有）
        List<Double> actualList = new ArrayList<>();
        
        if (prediction.getActualData() != null && !prediction.getActualData().isEmpty()) {
            JSONArray actualArray = JSON.parseArray(prediction.getActualData());
            for (int i = 0; i < actualArray.size(); i++) {
                JSONObject dataPoint = actualArray.getJSONObject(i);
                actualList.add(dataPoint.getDouble("value"));
            }
        } else {
            // 如果没有实际数据，填充null
            for (int i = 0; i < timeList.size(); i++) {
                actualList.add(null);
            }
        }
        
        result.put("timeList", timeList);
        result.put("predictedList", predictedList);
        result.put("actualList", actualList);
        
        return result;
    }
    
    /**
     * 使用随机森林算法进行预测（简化实现）
     * 
     * @param historicalData 历史数据
     * @param predictionPoints 预测点数
     * @return 预测结果
     */
    private List<Double> predictWithRandomForest(List<Double> historicalData, int predictionPoints)
    {
        // 注意：这是一个简化的随机森林预测实现
        // 实际应用中应使用真实的机器学习库，如Weka、Apache Spark MLlib等
        
        List<Double> result = new ArrayList<>();
        Random random = new Random();
        
        if (historicalData.isEmpty()) {
            return result;
        }
        
        // 计算历史数据的均值和标准差
        double sum = 0;
        for (Double value : historicalData) {
            sum += value;
        }
        double mean = sum / historicalData.size();
        
        double variance = 0;
        for (Double value : historicalData) {
            variance += Math.pow(value - mean, 2);
        }
        variance /= historicalData.size();
        double stdDev = Math.sqrt(variance);
        
        // 获取最近的趋势
        double trend = 0;
        if (historicalData.size() >= 2) {
            int lastIndex = historicalData.size() - 1;
            trend = (historicalData.get(lastIndex) - historicalData.get(lastIndex - 1)) / 2;
        }
        
        // 生成预测值
        double lastValue = historicalData.get(historicalData.size() - 1);
        
        for (int i = 0; i < predictionPoints; i++) {
            // 添加趋势和一些随机波动
            double nextValue = lastValue + trend + random.nextGaussian() * stdDev * 0.2;
            
            // 确保预测值在合理范围内
            nextValue = Math.max(0, nextValue); // 假设最小值为0
            
            result.add(nextValue);
            lastValue = nextValue;
        }
        
        return result;
    }
}